VIBE CODING - THE FUTURE OF PROGRAMMING
As a new joiner, I felt excited about my tasks and even more excited to step into vibe coding. It sounded fun… until I realized that AI covered only about 70% of the functionality — and the remaining 30% still didn’t meet my objective.
As juniors or less-experienced developers, we naturally feel a mix of excitement and anxiety about AI. It can write code we don’t yet know how to write ourselves, which definitely accelerates learning. But when code just appears without us understanding the underlying principles, we miss out on building debugging skills, learning fundamentals, and understanding architectural patterns. Eventually, this makes it harder to maintain and evolve code.
AI often generates “functional but horribly optimized” code unless we guide it through iterative improvement. AI can help us chop more trees, but someone still needs to decide which trees to cut and how to build a stable house from the lumber. Our judgment, strategic thinking, and mentorship matter more than ever.
Senior developers bring deep domain knowledge, intuition about what could go wrong, and the ability to make high-level technical decisions. That’s the 30% AI can’t do. They know the patterns, where to modify things, and how to navigate complexity. They use AI to accelerate what they already know, while juniors rely on it to learn what to do.
AI can generate code — but attention to detail cannot be AI-generated. Developers might use AI as a first drafter, pair programmer, or validator. Let AI handle the first draft, while we focus on architecting the solution and refining what it produces.
A line I read in the book “Vibe Coding” stayed with me. Tim O’Reilly, reflecting on decades of technological shifts, says
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we’re not facing the end of programming — but the end of programming as we know it today. Developer roles are not evaporating , but are evolving.
If we’re not actively engaging with why the AI is generating certain code, we actually learn less. Debugging AI-written code teaches us how to prompt better next time, avoid weak patterns, and strengthen both AI output and our own decision-making.
One durable skill that will always accelerate growth is the ability to seek feedback and learn from others. Strong communication skills are as important as ever — asking good questions, explaining problems clearly, whether to colleagues or to AI.
A powerful machine in unskilled hands can be wasted or even dangerous — but in capable hands, it’s transformative. I started learning to use AI as a tutor instead as an answer-vending machine.
The Golden Rules of Vibe Coding -
Vibe coding does feel like the future of programming, and I agree it has its place. It helps move fast and lowers the entry barrier, which is great. That said, there’s a real risk when people use it without fully understanding what the code is doing behind the scenes. Recently, a small mistake in a GitHub repository led a student to accidentally expose an API key, which resulted in a huge unexpected bill. Situations like this highlight the security concerns around blindly trusting generated code. There are clear pros and cons. Vibe coding works well to an extent, especially when you know exactly what you’re building and reviewing every part of it. For better control and security, shifting towards low-code approaches and proper code review makes more sense. Otherwise, the chances of security issues or hacks are much higher.
This perfectly shows that AI can support us, but real engineering still comes from solid fundamentals.